방법 비교

	Multinomial Logistic Regression	순서형 로지스틱 회귀
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	1966–1974	1980
창시자	Cox (1966); Theil (1969); formalized by McFadden (1974)	Peter McCullagh
유형	Generalized linear model	Ordinal regression / GLM
원전	Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. ISBN: 978-0471360933	McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142.
별칭	polytomous logistic regression, softmax regression, multinomial logit, nominal logistic regression	proportional-odds model, cumulative link model, ordered logit, OLR
관련	4	6
요약	Multinomial logistic regression extends binary logistic regression to outcomes with three or more unordered categories. It models the log-odds of each category relative to a chosen reference category as a linear function of the predictors, and estimates all parameters simultaneously via maximum likelihood. It is the standard choice when the dependent variable is nominal with multiple levels.	Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds.